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Navient is a computerized surgical navigation system intended as an aid for precisely locating anatomical structures in either open or percutaneous neurosurgical procedures.

The Navient system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the skull, can be identified relative to a CT, MR based model of the anatomy.

Indications:

Example procedures include but are not limited to:

• Cranial Procedures:
• Tumor resections
• · Cranial biopsies
• · Craniotomies
• · Pediatric Catheter Shunt Placement
• General Catheter Shunt Placement

Navient is an image quided navigational system intended to assist with preoperative planning and real-time positioning of surgical tools during stereotaxic procedures via (infrared) tracking technology. The system is essentially composed of a computerized main unit (computer), a Navient IR CameraBox, Navient cart, Navient navigation software, and corresponding accessory sets intended for specific clinical applications.

Navient's quidance function is based on the patient images acquired prior to the procedure, combined with optical measurements of the pose of navigated instruments relative to the patient's anatomy. To enable navigation, the reference instrument/accessory is attached to the patient to enable tracking of the patient's anatomy. The patient images are then spatially registered with the patient's anatomy by matching landmark locations marked on both the image and the patient, followed by matching a path traced by the user on the patient's anatomy with a model of patient's anatomical surface automatically generated from the image data.

Depending on the desired clinical application, the Navient system also includes the following instrument/accessory kits. These reusable instruments are intended to be sterilized/disinfected prior to use.

• Cranial Accessory Kit (955-NC-AKC) o
• Biopsy Accessory Kit (955-NC-AKB) O

Disposable tracker instruments are also available based on user preference.

The provided document is a 510(k) summary for the ClaroNav Navient Image Guided Navigation System, Cranial. This document describes the device, its intended use, technological characteristics, and performance data to demonstrate substantial equivalence to a predicate device.

Acceptance Criteria and Device Performance:

The primary acceptance criteria for the Navient system is its accuracy, specifically in terms of positional and angular error. The document states: "Navient has been validated to a mean positional error of ≤ 2.0 mm and a mean anqular error of ≤ 2.0deq."

Here's a table summarizing the acceptance criteria and reported device performance:

Study Proving Device Meets Acceptance Criteria:

The study proving the device meets the acceptance criteria is described under the "Performance Data" section, specifically "Full system accuracy bench testing (overall accuracy)".

1. A table of acceptance criteria and the reported device performance:
(See table above)

2. Sample size used for the test set and the data provenance:

• Sample Size for the Test Set: The document does not explicitly state the numerical sample size (e.g., number of measurements or trials) for the "Full system accuracy bench testing." It mentions using "representative phantoms."
• Data Provenance: The data is based on bench testing using phantoms. The country of origin of the data is not specified, but the applicant is from Canada. The study is a prospective experimental study conducted by the manufacturer to demonstrate performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This is a bench testing study, not an AI or human-in-the-loop clinical study where expert review of images or clinical outcomes would establish ground truth. The "ground truth" for this accuracy testing would be the known, precise measurements and positions of the targets on the phantoms. Therefore, no external experts are typically involved in establishing this type of ground truth for a physical device's mechanical/measuring accuracy. The expertise lies in the engineering and quality control personnel performing the measurements.

4. Adjudication method for the test set:

Not applicable in the context of bench testing device accuracy. Adjudication typically refers to resolving discrepancies among multiple human readers or ground truth experts.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No, an MRMC comparative effectiveness study was not done. This document pertains to the clearance of a surgical navigation system, which primarily aids surgeons in real-time positioning based on pre-acquired images. It is not an AI-based diagnostic tool intended to assist human readers in interpreting medical images or improving diagnostic accuracy.
• The study described is a bench study evaluating the physical accuracy of the navigation system, not the diagnostic performance or reader improvement.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Yes, in a sense, the "Full system accuracy bench testing" represents the standalone performance of the device's navigational accuracy. The measurements of positional and angular error were likely performed by the device and then compared against the known ground truth of the phantom. This is the performance of the algorithm and hardware working together in a controlled environment.
• It's important to differentiate this from "standalone AI performance" if the device were an AI diagnostic algorithm. Here, "standalone" refers to the device's ability to accurately track and report positions, irrespective of a surgeon's subjective assessment.

7. The type of ground truth used:

The ground truth used for the full system accuracy bench testing was engineered ground truth based on known, precise measurements of target points on "representative phantoms." These phantoms are designed with known spatial relationships that serve as the gold standard for accuracy verification.

8. The sample size for the training set:

• Not applicable. This document describes a surgical navigation system, not an AI algorithm that undergoes machine learning training. Therefore, there is no "training set" in the context of deep learning models.
• The "training" for such a device involves calibration, manufacturing processes, and software development, which are different from data-driven machine learning training.

9. How the ground truth for the training set was established:

• Not applicable. As stated above, there is no "training set" in the machine learning sense for this device.
• For the development and calibration of the device, the inherent accuracy of manufacturing, calibration tools, and metrology standards would serve as the "ground truth" for ensuring the device performs as designed.

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